Tree fall management

ABSTRACT

An approach to tree fall risk management. This approach may identify a tree in a given location. Historical data associated with the geographic location may be received in the approach. A current condition or status of the tree may be identified by the approach. The approach may analyze the foreseeable weather forecast or weather conditions in conjunction with the status of the identified tree. The approach may generate a risk score based on the information received and analyzed. The risk score may indicate the tree is likely to fall and cause damage. The approach may result in tree fall mitigation action can be generated based on the risk score.

BACKGROUND OF THE INVENTION

The present invention relates generally to tree fall and foliagemanagement and more specifically, to generating mitigation actions forpotential tree falls.

Falling trees and foliage cause billions of dollars in damage every yearto property and injury or death to persons and animals. In many casesfalling trees and branches can cause downed powerlines, resulting indisruptions to commerce and prevent safety services from safelytraversing roadways. Severe weather is one of the main factors whichcause trees and branches to fall and cause damage. Many trees aretrimmed to prevent potential damage due to severe weather, but in somecases unexpected factors can cause a tree to fall resulting in damage toproperty and injury or death to persons or animals.

SUMMARY

Embodiments of the present disclosure include a computer-implementedmethod and a computer-system for generating a tree fall mitigationaction. The embodiments may include the following identify a tree at ageographic location, based on an image. Receiving historical data forthe geographic location. Identifying one or more conditions for thetree. Identifying a weather risk indicator for the geographic location.Generating a tree fall risk score, based on the historical data, one ormore tree conditions, and the weather risk indicator. Additionally,embodiments may include, generating a tree fall mitigation action, ifthe tree fall risk score is above a threshold.

The above summary is not intended to describe each illustratedembodiment of every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram generally depicting a tree fallrisk management environment, in accordance with an embodiment of thepresent invention.

FIG. 2 is a functional block diagram depicting a tree fall managementengine, in accordance with an embodiment of the present invention.

FIG. 3 is a flowchart depicting operational steps of a method forgenerating a tree fall mitigation action, in accordance with anembodiment of the present invention.

FIG. 4 is a functional block diagram of an exemplary computing systemwithin a tree fall management environment, suitable for generating atree fall mitigation action, in accordance with an embodiment of thepresent invention.

FIG. 5 is a diagram depicting a cloud computing environment, inaccordance with an embodiment of the present invention.

FIG. 6 is a functional block diagram depicting abstraction model layers,in accordance with an embodiment of the present invention.

While the embodiments described herein are amenable to variousmodifications and alternative forms, specifics thereof have been shownby way of example in the drawings and will be described in detail. Itshould be understood, however, that the particular embodiments describedare not to be taken in a limiting sense. On the contrary, the intentionis to cover all modifications, equivalents, and alternatives fallingwithin the spirit and scope of the disclosure.

DETAILED DESCRIPTION

The embodiments depicted and described herein recognize the need forpredicting risks and potential damage associated with falling trees andbranches and generating tree fall amelioration actions. In anembodiment, a tree may be identified in a specific geographic location.Historical data relating to weather and soil conditions for the locationmay be received. A condition of the identified tree may be recognized.The predicted weather for a time frame can be determined and a tree fallrisk score can be generated for the predicted weather, factoring in thecondition of the identified tree and the historical data for thelocation. If the tree fall risk score is above a predeterminedthreshold, a tree fall amelioration action can be generated.

In some embodiments, the tree may be identified with an individual imagefrom a camera or a video recording from a video camera (e.g. webcam,smart glasses, cellular telephone, tablet, etc.). The tree can beidentified utilizing a pixel-level classifier. The pixel levelclassifier can be an artificial intelligence model based on a deepneural network (“DNN”) (e.g. a convolutional neural network). The pixellevel classifier can identify one or more trees within the image orvideo recording.

In some embodiments, a tree fall risk score can be generated by amachine learning model, where the machine learning model is a DNN.Multiple factors may be feed into the DNN, including the historical datafor the location, the condition of the identified tree, predictedweather conditions, and real-time data feeds from sensors locatedproximally to the identified tree. Further, the tree fall risk score canbe generated for one or more trees within the area based on theproximity of other trees to the identified tree.

Additional embodiments may further include one or more sensors capableof reporting a condition associated with the identified tree. Thesesensors may include soil moisture sensors, accelerometers, thermometers,anemometers, seismographs, etc.

Embodiments may include a physics simulation engine capable ofpredicting potential damage caused by one or more falling trees, basedon the weather predictions for the location. The physics simulationengine may be a machine learning model trained with tree images andsevere weather data and resulting outcomes. Historical location data mayinclude information of permanent and semi-permanent property locatednear the identified tree. Additionally, in embodiments where a real-timeimage feed is available, a pixel level classifier may identify transientobjects which can be factored into the physics simulation engine andpredict potential damage.

In describing embodiments in detail with reference to the figures, itshould be noted that references in the specification to “an embodiment,”“other embodiments,” etc., indicate that the embodiment described mayinclude a particular feature, structure, or characteristic, but everyembodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, describing a particularfeature, structure or characteristic in connection with an embodiment,one skilled in the art has the knowledge to affect such feature,structure or characteristic in connection with other embodiments whetheror not explicitly described.

FIG. 1 is a functional block diagram depicting, generally, a tree fallrisk management environment 100. Tree fall risk management environment100 comprises a tree fall management engine 104 operational on a servercomputer 102, tree profile database 108 and climate database 110 storedon server computer 102, tree data sensor 112, and network 106 supportingcommunications between the server computer 102, tree data sensor 112 andadditional computing devices (not shown).

Server computer 102 can be a standalone computing device, managementserver, a web server, a mobile computing device, or any other electronicdevice or computing system capable of receiving, sending, and processingdata. In other embodiments, server computer 102 can represent a servercomputing system utilizing multiple computers as a server system. Inanother embodiment, server computer 102 can be a laptop computer, atablet computer, a netbook computer, a personal computer, a desktopcomputer, or any programmable electronic device capable of communicatingwith other computing devices (not depicted) within tree fall riskmanagement environment 100 via network 106.

In another embodiment, server computer 102 represents a computing systemutilizing clustered computers and components (e.g., database servercomputers, application server computers, etc.) that act as a single poolof seamless resources when accessed within tree fall risk managementenvironment 100. Server computer 102 can include internal and externalhardware components, as depicted and described in further detail withrespect to FIG. 4.

Tree Fall Management Engine 104 can be a module for receiving datainputs, and generating tree fall amelioration actions with machinelearning models and deep neural networks. It should be noted, in someembodiments there can be multiple instances of tree fall managementengine 104 operating on multiple target servers or within a servercenter. Some embodiments can allow for the user to review the tree fallamelioration action and verify if it is correct, prior to executing thetree fall amelioration action, via a notification (e.g., within a smartphone application, e-mail, web-portal, etc.). Additionally, embodimentsof the invention can be configured using historical data andeffectiveness of past tree fall amelioration actions to ensure thegeneration of efficient and cost-effective and proper ameliorationactions.

Network 106 can be, for example, a local area network (LAN), a wide areanetwork (WAN) such as the Internet, or a combination of the two, and caninclude wired, wireless, or fiber optic connections. In general, network106 can be any combination of connections and protocols that willsupport communications between server computer 102, tree data sensor112, image capture device 114 and other computing devices (notdepicted).

Tree profile database 108 can be a database that may contain datarelating to one or more trees. The data can include informationassociated with a tree including species, geographic location, age ofthe tree, topology of the landscape surrounding the tree, angle of thetree's trunk relative to the ground or the branches relative to theground, proximity of the tree or branches to property, power lines,livestock or parked vehicles, and soil composition. Additionalinformation can include if the tree is damaged due to broken branches ortree limbs, rot, pest infestation, or disease. In some embodiments, treeprofile database 108 can contain information relating to the expectedtensile strength of the wood for the specific tree and trees surroundingthe tree. In various embodiments, information relating to the age,diameter, and protected status can be contained in tree profile database108. Further, if a tree has protected status information (e.g. a permitis required for trimming or falling the tree due to the tree's size,species, etc.) contact information associated with the protection entity(e.g. a local/state/federal government department), can be contained intree profile database 108.

Climate database 110 can be a database that contains historical datarelated to weather for the geographic area associated with a tree. Forexample, the database can contain precipitation amount, wind speed,temperatures, flooding information, drought information, prevailing winddirection and sunlight or ultraviolet light exposure. Additionalinformation may relate to pest or insect infestations associated withtrees or soil in the geographic area. Tree data sensor 112 can be asensor capable of recording environmental conditions and taking realtime readings of environmental conditions. Tree data sensor 112 can alsobe capable of sending the recorded and real time readings to tree fallmanagement engine 104. It should be noted, tree data sensor 112 can beassociated with one or more trees in a given area. Tree data sensor 112can be configured with data relating to the proximity (distance,orientation to tree, etc.) of a tree or trees of interest. Examples ofenvironmental readings can include temperature, precipitation amount,soil moisture content, wind speed, seismic activity, tree motion, treelimb angles/growth, tree disease, etc. . . . Further, the readings andrecordings from tree data sensor 112 can be stored in climate database110 and tree profile database 108. While one tree data sensor is shownin FIG. 1, this is for illustration purposes only, as multiple tree datasensors (i.e. 1, 2, n . . . n+1) capable of reading one or moreenvironmental conditions may be present in an embodiment.

Image capture device 114 can be a device capable of capturing a stillimage or recording of a tree. Image capture device can be a camera, webcamera, satellite with image recording capabilities, virtual realityportal, augmented reality glasses, etc. The image can be a still printimage, digital image, real-time data feed of a location, satelliteimage, or the like. Further, in an embodiment, image capture device maybe stationary or mobile, capable of capturing images of a tree or treesin a given location at multiple angles. While one image capture deviceis shown in FIG. 1, this is for illustration purposes only, as multipleimage capture devices may be present in an embodiment.

FIG. 2 is a functional block diagram 200 depicting tree fall managementengine 104. Operational on tree fall management engine 104 is treedetection module 202, risk score generation module 204, and mitigationaction generation module 206.

Tree detection module 202 can be a computer module that can identify atree from an image. In some embodiments, tree detection module 202 candetect one or more trees and other objects, property, or structures fromone or more images received from image capture device 114. For example,tree detection module 202 may receive an image from a satellite withgeographic information. Tree detection module 202 may use a pixelclassifier to determine an individual tree and objects or structuresaround the tree. In some embodiments, the pixel classifier can be a deepneural network (e.g. convolutional, long-short term memory, etc.)trained on body of labelled satellite photos of trees. In someembodiments, tree detection module may receive live images from one ormore video cameras (e.g., surveillance cameras, webcams, smart phones)and identify one or more trees from the received live images using animage classification model using pixel classification. Additionally,tree detection module 202 can identify permanent objects within an imagesuch as structures (e.g. residences, outbuildings, barns, livestockcorrals, garages, etc.) and/or semi-permanent objects, (e.g. vehicles,tractors, trailers, mobile homes, sheds etc.).

Risk score generation module 204 is a computer module that can generatea risk associated with a tree falling and/or tree limb falling based onidentification of one or more trees and an associated environmentalfactor. In an embodiment, risk score generation module 204 can receivean identified tree from tree detection module 202. Risk score generationmodule 204 can retrieve data related to the identified tree from treeprofile database 108 and/or climate database 110. Additionally, riskscore generation module 204 can receive current environmental conditionsfrom tree data sensor 112. Further, in some embodiments, risk scoregeneration module may be capable of predicting future weather conditions(e.g., temperature, precipitation, wind, flooding, ice, drought, etc.)using a machine learning model. In various embodiments, risk scoregeneration module 204 can receive or obtain weather forecasts fromvarious government and commercial sources (e.g. The National WeatherService, The National Oceanic and Atmospheric Administration, TheWeather Channel®, local news channels, etc.) Additionally, risk scoregeneration module 204 can have physics simulation modelling capabilitiesand predict the fall path of a tree or tree limb, based on factorsassociated with the current and predicted environmental conditions.Finally, risk score generation model 204 can generate a risk score basedon a multitude of factors using a deep neural network trained with theabove-mentioned data.

In an example, tree data sensor 112 may be a soil moisture sensor. Treedata sensor 112 may be associated with one or more identified trees in aknown location. Risk score generation module 204 may receive or retrievedata from local climate database 110 relating to the most recent weatherfor the area where the identified trees are located, and data related tothe one or more identified trees from tree data sensor 112.Additionally, the weather for the location can be predicted for theimmediate future (e.g. current time to seven days from the currenttime). In this example, there has been heavy rainfall in the location(reported by local climate database) and the soil moisture sensor isreading very saturated soil. Risk score generation module 204 retrievesdata from tree data sensor 112 reports the identified tree has thefollowing conditions of a lean and root disease. Predicted weather froma weather model is generated or received at risk score generation module204. In the current example, there will be sustained high speed windsand heavy precipitation. The information is factored into the physicssimulation module and results in a risk score where the physics modelpredicts the possibility of the tree falling due to the factors receivedby risk score generation module 204. Further, the physics model candetermine the path which a tree or tree limb may fall and any propertyor items the tree or tree limb may fall on. In the immediate example, arisk score can be generated depending on the chance the tree or treelimb will fall and the resulting damage. If the risk score is above apredetermined threshold the risk score can be sent to mitigation actiongeneration module.

In an embodiment, risk score generation module 204 can convert the datait receives from the various sources described above into risk vectors,which are input into a neural network. The neural network can output arisk score. The neural network can be trained with a corpus of tree falldata including weather, species, location, soil composition, foliage onthe tree, angle of tree trunk to the ground. Further, in someembodiments, the neural network can be updated and further trained withthe data from real-time inputs. Additionally, if real time monitoringsends images, via a data feed, risk score generation module 204 canmonitor one or more trees in a location and update the risk score indynamically. For example, the risk score can be generated continuously,in preset time intervals (e.g., every hour, day, week, etc.) or onidentified weather events (forecasted severe weather).

Mitigation action generation module 206 is a computer module that cangenerate an action which will prevent or mitigate damage caused by apredicted falling tree or tree limb. In some embodiments, mitigationaction generation module 206 can receive a risk score from risk scoregeneration module 204. A mitigation action can be sending a notificationto a user's computing device (phone, tablet, computer, etc.) via email,text, or pop-up notification, if associated with an application on asmart device. In some embodiments, a notification can be a notificationto move a semi-permanent object or personal property (e.g., car,recreational vehicle, boat, bicycle). In other embodiments, thenotification can be a prompt to schedule a tree service to cut down thetree or tree limb which is predicted to cause the damage to a structure.In other embodiments, the mitigation action may be a notification toprotect the ground surrounding a tree from further saturation.

FIG. 3 is a flowchart depicting operational steps of a method forgenerating a tree fall mitigation action 300. At step 302, identify oneor more trees in a given location by tree detection module 202, from animage received by image capture device 114 (as described above withrespect to FIG. 2). For example, in an embodiment an image may bereceived from a user's smart phone or smart glasses. The image can beinput into a convolutional neural network. The convolutional neuralnetwork can identify a tree from the input image. The image may possessgeographic information (e.g. longitude and latitude information), whichcan be from geospatial positioning system or input from the user.Further, other items may be identified by the convolutional neuralnetwork such as vehicles, structures, or personal property.

At step 304, receive historical data associated with the identified treeor trees by risk score generator module 204. Historical data associatedwith the identified trees can be received from climate database 110 orcan be input by a user with a smart device or computing device to riskscore generation module 204. In some embodiments, historical data can bereceived or retrieved from tree profile database by risk scoregeneration module 204.

At step 306, identify one or more conditions associated with theidentified tree or trees at risk score generation module 204. In anembodiment, a sensor may identify a condition associated with the tree,such as height of the tree, soil moisture, seismic activity, lightexposure, etc. In some embodiments, more than one condition or statuscan be identified by a sensor or can be input into by a user. Conditionsinput by a user could include, disease, pest infestation, rot, damage totree limbs, erosion surrounding roots, etc.

At step 308, identify a weather risk indicator with risk scoregeneration module 204. In some embodiments, a weather prediction modelcan predict weather multiple days in advance (e.g. from the current timeup to seven days out). Additionally, a weather prediction model canforecast severe weather. In some embodiments, only severe weather willbe identified by risk score generation module 204 (e.g., ice storm,hurricane, tornado, sandstorm, heavy rain or snow, etc.).

At step 310, generate a tree fall risk score with risk score generationmodule 204. In some embodiments, the factors identified above can beconverted into risk vectors and input into a neural network to generatea risk score. In other embodiments, the risk vectors can be input into aphysics model, where the identified tree and surrounding objects aresimulated and a prediction for how likely it is a tree will fall or atree limb will fall. Additionally, in some embodiments, the fall path ofthe tree or tree limb can be predicted and whether or not objects inproximity to the fall path will be damaged. Further, in someembodiments, the damage to the surrounding landscape can be predicteddue to fall path or potential uprooting of trees (e.g., for golf coursesand parks). A tree fall risk score can be the calculation of thelikelihood of the identified tree falling, the chance the identifiedtree will cause damage to property, and/or the amount of damage a treefall will cause (e.g. dollar value or amount of property damaged).

At step 312, determine if the risk score is above a threshold. In someembodiments, risk score is received by mitigation action generationmodule 206, where mitigation action generation module determined if therisk score is above a threshold. In another embodiment, risk scoregeneration module 204 determines if the risk score is above a thresholdbefore sending the risk score to mitigation action generation module206. In either embodiment, if the risk score is below a threshold theprocess ends. If the risk score is above a threshold, the processcontinues.

In some embodiments, the threshold can be static or dynamic. If it is adynamic threshold, multiple factors can determine the threshold,including time of year, geographic location, climate, insurance premiumsor deductibles, and objects surrounding the identified tree. In someembodiments, the risk score can be used to update the physics model orneural network used to generate the risk score.

At step 314, generate a tree fall risk mitigation action at mitigationaction generation module 206. In some embodiments, a notification can besent to a user's smart device to move mobile or semi-permanent objects.For example, if risk score generation module predicts a damaged treelimb will fall on a car during an upcoming storm, the notification mayinform a user to relocate the car. In another example, if a pop-up shedis in the likely path of a tree falling a notification can be send to auser to relocate or dismantle the shed to prevent damage. In otherembodiments, if it is determined a tree fall will likely cause damage toa permanent structure (e.g., home, garage, workshop), mitigation actiongeneration module 206 can schedule a tree service to come and cut downthe tree or add supports to the tree. Further, in various embodiments,tree fall risk mitigation action can comprise notification of protectionentities for specific trees identified as protected due of thelikelihood of damage due from the tree. In addition to notifying theprotection entity, mitigation action generation module 206 can generatea permit application for trimming or falling of the tree.

Further, in some embodiments, at step 314, tree fall risk mitigationaction can include notification of one or more insurance companies. Theinsurance company may be identified by a user in a registration processto access the tree fall risk mitigation action via a smart device app orweb-portal. The data generated by a physics model and predicted propertydamage can be transmitted to the insurance company. In some embodiments,tree fall mitigation action notifications and associated actions can betransmitted for premium and liability calculations.

In some embodiments, at step 314, the user may receive a prompt toaccept the generated mitigation action or mitigation action generationmodule 206 can automatically schedule the action to occur if the userhas configured the module to pre-accept the actions generated. The userresponse can be used to update risk score calculation on mitigationaction generation module 206 for future tree fall risk mitigationactions and notifications of parties.

FIG. 4 depicts a functional block diagram of an exemplary computersystem 400 within tree fall risk management environment 100. Computersystem 400 includes communications fabric 412, which providescommunications between computer processor(s) 414, memory 416, persistentstorage 418, network adaptor 428, and input/output (I/O) interface(s)426. Communications fabric 412 can be implemented with any architecturedesigned for passing data and/or control information between processors(such as microprocessors, communications and network processors, etc.),system memory, peripheral devices, and any other hardware componentswithin a system. For example, communications fabric 412 can beimplemented with one or more buses.

Computer system 400 includes processing unit 414, cache 422, memory 416,network adaptor 428, input/output (I/O) interface(s) 426 andcommunications fabric 412. Communications fabric 412 providescommunications between cache 422, memory 416, persistent storage 418,network adaptor 428, and input/output (I/O) interface(s) 426.Communications fabric 412 can be implemented with any architecturedesigned for passing data and/or control information between processors(such as microprocessors, communications and network processors, etc.),system memory, peripheral devices, and any other hardware componentswithin a system. For example, communications fabric 412 can beimplemented with one or more buses or a crossbar switch.

Memory 416 and persistent storage 418 are computer readable storagemedia. In this embodiment, memory 416 includes persistent storage 418,random access memory (RAM) 420, cache 422 and program module 424. Ingeneral, memory 416 can include any suitable volatile or non-volatilecomputer readable storage media. Cache 422 is a fast memory thatenhances the performance of processors 414 by holding recently accesseddata, and data near recently accessed data, from memory 416. As will befurther depicted and described below, memory 416 may include at leastone of program module 424 that is configured to carry out the functionsof embodiments of the invention.

The program/utility, having at least one program module 424, may bestored in memory 416 by way of example, and not limiting, as well as anoperating system, one or more application programs, other programmodules, and program data. Each of the operating systems, one or moreapplication programs, other program modules, and program data or somecombination thereof, may include an implementation of a networkingenvironment. Program module 424 generally carries out the functionsand/or methodologies of embodiments of the invention, as describedherein.

Program instructions and data used to practice embodiments of thepresent invention may be stored in persistent storage 418 and in memory416 for execution by one or more of the respective processors 414 viacache 422. In an embodiment, persistent storage 418 includes a magnetichard disk drive. Alternatively, or in addition to a magnetic hard diskdrive, persistent storage 418 can include a solid state hard drive, asemiconductor storage device, read-only memory (ROM), erasableprogrammable read-only memory (EPROM), flash memory, or any othercomputer readable storage media that is capable of storing programinstructions or digital information.

The media used by persistent storage 418 may also be removable. Forexample, a removable hard drive may be used for persistent storage 418.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of persistent storage418.

Network adaptor 428, in these examples, provides for communications withother data processing systems or devices. In these examples, networkadaptor 428 includes one or more network interface cards. Networkadaptor 428 may provide communications through the use of either or bothphysical and wireless communications links. Program instructions anddata used to practice embodiments of the present invention may bedownloaded to persistent storage 418 through network adaptor 428.

I/O interface(s) 426 allows for input and output of data with otherdevices that may be connected to each computer system. For example, I/Ointerface 426 may provide a connection to external devices 430 such as akeyboard, keypad, a touch screen, and/or some other suitable inputdevice. External devices 430 can also include portable computer readablestorage media such as, for example, thumb drives, portable optical ormagnetic disks, and memory cards. Software and data used to practiceembodiments of the present invention can be stored on such portablecomputer readable storage media and can be loaded onto persistentstorage 418 via I/O interface(s) 426. I/O interface(s) 426 also connectto display 432.

Display 432 provides a mechanism to display data to a user and may be,for example, a computer monitor or virtual graphical user interface.

The components described herein are identified based upon theapplication for which they are implemented in a specific embodiment ofthe invention. However, it should be appreciated that any particularcomponent nomenclature herein is used merely for convenience, and thusthe invention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The present invention may be a system, a method and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It is understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality and operation of possible implementations ofsystems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

FIG. 5 is a block diagram depicting a cloud computing environment 50 inaccordance with at least one embodiment of the present invention. Cloudcomputing environment 50 includes one or more cloud computing nodes 10with which local computing devices used by cloud consumers, such as, forexample, personal digital assistant (PDA) or cellular telephone 54A,desktop computer 54B, laptop computer 54C, and/or automobile computersystem 54N may communicate. Nodes 10 may communicate with one another.They may be grouped (not shown) physically or virtually, in one or morenetworks, such as Private, Community, Public, or Hybrid clouds asdescribed hereinabove, or a combination thereof. This allows cloudcomputing environment 50 to offer infrastructure, platforms and/orsoftware as services for which a cloud consumer does not need tomaintain resources on a local computing device. It is understood thatthe types of computing devices 54A-N shown in FIG. 6 are intended to beillustrative only and that computing nodes 10 and cloud computingenvironment 50 can communicate with any type of computerized device overany type of network and/or network addressable connection (e.g., using aweb browser).

FIG. 6 is a block diagram depicting a set of functional abstractionmodel layers provided by cloud computing environment 50 depicted in FIG.5 in accordance with at least one embodiment of the present invention.It should be understood in advance that the components, layers, andfunctions shown in FIG. 6 are intended to be illustrative only andembodiments of the invention are not limited thereto. As depicted, thefollowing layers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and tree fall risk management 96.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration but are not intended tobe exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The terminology used herein was chosen to best explain the principles ofthe embodiment, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A computer-implemented method for tree fall riskmanagement, the computer-implemented method comprising: identifying, byone or more processors, a tree at a geographic location, based on animage; receiving, by the one or more processors, historical data for thegeographic location; identifying, by the one or more processors, one ormore conditions for the tree; identifying, by the one or moreprocessors, a weather risk indicator for the geographic location;generating, by the one or more processors, a tree fall risk score, basedon the historical data, one or more tree conditions, and the weatherrisk indicator; and responsive to the tree fall risk score being above athreshold, generating, by the one or more processors, a tree fallmitigation action.
 2. The computer-implemented method of claim 1,wherein: identifying further comprises: receiving, by the one or moreprocessor, the image with geographic data from an image capture device,wherein the geographic data is from a geospatial positioning systemwithin the image capture device; and inputting, by the one or moreprocessors, the image into a pixel classifier.
 3. Thecomputer-implemented method of claim 2, wherein the pixel classifier isa convolutional neural network trained with a corpus of tree images. 4.The computer-implemented method of claim 1, wherein: generating a riskscore further comprises: converting, by the one or more processors, thehistorical data, the one or more tree conditions, and the weather riskindicator into risk vectors; and inputting, by the one or moreprocessors, the risk vectors into a deep neural network.
 5. Thecomputer-implemented method of claim 4, wherein, the deep neural networkis trained by a corpus of historical climate data and tree profiles. 6.The computer-implemented method of claim 2, further comprising:identifying, by the one or more processors, one or more objects from theimage received from image capture device, wherein the objects arestructures and personal property.
 7. The computer-implemented method ofclaim 6, wherein generating a risk score further comprises: simulating,by the one or more processors, a tree fall path with a physicssimulation model for the identified tree; and determining, by the one ormore processors, damage to the one or more objects.
 8. Thecomputer-implemented method of claim 1, wherein generating the tree fallrisk score is performed by a cloud-based machine learning model.
 9. Thecomputer-implemented method of claim 2, wherein the image capture deviceis a live-stream feed of the identified tree.
 10. Thecomputer-implemented method of claim 2, wherein generated the risk scoregeneration module is updated based on a user configured interval.
 11. Acomputer system for tree fall risk management, the system comprising: acomputer processor; a computer readable storage media; computer programinstructions; the computer program instructions being stored on the oneor more computer readable storage media for execution by the one or morecomputer processors; and the computer program instructions includinginstructions to: identify a tree at a geographic location, based on animage; receive historical data for the geographic location; identify oneor more conditions for the tree; identify a weather risk indicator forthe geographic location; generate a tree fall risk score, based on thehistorical data, one or more tree conditions, and the weather riskindicator; and responsive to the tree fall risk score being above athreshold, generate a tree fall mitigation action.
 12. The computersystem of claim 11, wherein: identify further comprises instructions to:receive the image with geographic data from an image capture device,wherein the geographic data is from a geospatial positioning systemwithin the image capture device; and input the image into a pixelclassifier.
 13. The computer system of claim 12, wherein the pixelclassifier is a convolutional neural network trained with a corpus oftree images.
 14. The computer system of claim 11, wherein: generating arisk score further comprises instructions to: convert the historicaldata, the one or more tree conditions, and the weather risk indicatorinto risk vectors; and input the risk vectors into a deep neuralnetwork.
 15. The computer system of claim 14, wherein, the deep neuralnetwork is trained by a corpus of historical climate data and treeprofiles.
 16. The computer system of claim 11, further comprisinginstructions to: identify one or more object from the image receivedfrom image capture device, wherein the objects are structures andpersonal property.
 17. The computer system of claim 16, whereingenerating a risk score further comprises instructions to: simulate atree fall path with a physics simulation model for the identified tree;and determine damage caused due to the tree fall path to the one or moreobjects.
 18. The computer system of claim 11, wherein generating thetree fall risk score is performed by a cloud-based machine learningmodel.
 19. The computer system of claim 12, wherein the image capturedevice is a live-stream feed of the identified tree.
 20. The computersystem of claim 12, wherein the generated risk score is updated based ona user configured interval.